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This paper is concerned with the problem of adaptive output feedback quantised tracking control for a class of stochastic nonstrict-feedback nonlinear systems with asymmetric input saturation. Especially, both input and output signals are quantised by two sector-bounded quantisers. In order to solve the technical difficulties originating from asymmetric saturation nonlinearities and sector-bounded quantisation errors, some special technique, approximation-based methods and Gaussian error function-based continuous differentiable model are exploited. Meanwhile, an observer including the quantised input and output signals is designed to estimate the states. Then, a novel output feedback adaptive quantised control scheme is proposed to ensure that all signals in the closed-loop system are 4-moment (2-moment) semi-globally uniformly ultimately bounded while the output signal follows a desired reference signal. Finally, the effectiveness and applicability of the design methodology is illustrated with two simulation examples. [ABSTRACT FROM AUTHOR]

The problem of output feedback adaptive tracking control is studied for a class of stochastic nonlinear time-delay systems in which the measured output and input signals are quantized by two sector-bounded quantizers respectively. An observer including the quantized input and output signals is designed to estimate the unknown system states, and the unknown system functions with less restrictions are dealt with by using the neural network’s (NN) approximation. By combining the backstepping technique and the Lyapunov–Krasovskii method, an observer-based adaptive neural quantized tracking control scheme is presented for this class of systems. The stability analysis indicates that the tracking error can converge to a small neighborhood of the origin while all closed-loop signals are 4-moment (or 2-moment) semi-globally uniformly ultimately bounded (SGUUB). Finally, two illustrative examples are provided to demonstrate the feasibility and effectiveness of the proposed design methodology. [ABSTRACT FROM AUTHOR]

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. [Copyright &y& Elsevier]